Association Between Intake of Ultra-Processed Food and Hours Worked: An Analysis of 2015-2018 NHANES
Bibliographic record
Abstract
Ultra-processed foods (UPFs) are foods that are typically ready-to-heat or eat and usually contain additives and substances not commonly used in food preparation. They make up around 60% of caloric intake in the US, but research has linked them with numerous detrimental effects on humans. This paper investigated the association between the number of hours worked per week and UPF consumption in the US to determine if addressing hours worked could potentially limit UPF intake. Though researchers have identified consequences of frequent UPF intake, not much is known about its possible causes. Using 2015-2018 NHANES data, ordinal logistic regression was performed to examine the relationship of interest. The major findings were that (1) UPFs contributed to around 65.2% of daily caloric intake in the US, and (2) there was no significant relationship between hours worked and UPF consumption. The high percent contribution reveals that UPFs make up a significant portion of American diets, and the observed lack of association aligns with previous findings that hours worked may not significantly impact feelings of time pressure (and resulting food choices). However, these analyses are limited in that NHANES does not categorize foods as UPF/non-UPF beforehand and potentially influential variables that were not asked about could not be accounted for. Overall, the high rate of consumption reinforces the need for more research about UPF intake reduction strategies, and the non-significant relationship of interest suggests that hours worked may not be an effective variable to analyze for its impact on UPF consumption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".